At Clinical Brain Lab, we use open and widely adopted research software to preprocess, analyze, visualize, and synthesize behavioural, physiological, and brain imaging data.
MRI Signal Analysis
MRI analysis often involves several steps: converting scanner data into research-ready formats, quality control, anatomical segmentation, spatial registration, task or resting-state modeling, diffusion modeling, and group-level statistics. The lab draws on established MRI toolboxes depending on the study design, modality, and analysis question.
MATLAB-based analysis for fMRI, PET, EEG, and MEG. SPM is commonly used for image preprocessing, spatial normalization, statistical parametric mapping, general linear modeling, and group-level inference across task-based and structural imaging studies.
FSL provides tools for structural MRI, task fMRI, resting-state fMRI, diffusion MRI, registration, segmentation, statistics, and visualization. It is useful for robust preprocessing and analysis pipelines across several MRI modalities.
AFNI is a suite for anatomical, functional, and diffusion MRI processing, analysis, quality control, and display. It is especially useful for interactive data inspection, flexible command-line workflows, and detailed fMRI preprocessing and modeling.
fMRIPrep
fMRIPrep is a standardized, reproducible preprocessing pipeline for task-based and resting-state functional MRI. It integrates tools from packages such as FSL, ANTs, FreeSurfer, and AFNI to generate analysis-ready outputs with quality reports.
NiMARE is a Python environment for coordinate- and image-based neuroimaging meta-analysis. It supports meta-analytic workflows, annotation, decoding, coactivation modeling, and reproducible synthesis of neuroimaging findings.
SUIT is an SPM add-on for cerebellar and brainstem imaging analysis. It supports cerebellar isolation, normalization to SUIT space, atlas-based summaries, and flatmap visualization for studies that require more precise cerebellar anatomy.
Magnetic Resonance Spectroscopy (MRS) Analysis
MRS analysis focuses on spectral preprocessing, fitting, quantification, tissue correction, and reporting of metabolite estimates. The right tool depends on the sequence, vendor format, reproducibility needs, and whether the workflow is single-voxel, MRSI, edited MRS, or functional MRS.
- FSL-MRS is an end-to-end spectroscopy toolbox for conversion, preprocessing, basis simulation, fitting, quantification, and visualization.
- LCModel is a long-standing package for automatic quantification of in vivo proton MR spectra.
- jMRUI provides time-domain analysis and quantification tools for clinical and biomedical MRS and MRSI data.
- Osprey is an open-source toolbox for processing, modeling, quantification, and visualization of in vivo MRS data.
- TARQUIN provides automated quantification for in vivo proton MRS and related NMR spectroscopy data.
Neuroimaging Meta-Analysis
Coordinate-based and image-based meta-analysis tools help synthesize findings across published neuroimaging studies. These tools are useful when the research question depends on convergence across many independent experiments rather than a single dataset.
- GingerALE is BrainMap’s application for activation likelihood estimation (ALE) meta-analysis of coordinates in Talairach or MNI space.
- BrainMap maintains databases and software resources for coordinate-based meta-analysis of human brain function and structure.
- Neurosynth supports large-scale automated synthesis of published fMRI studies and downloadable term-based meta-analytic maps.
- NiMARE supports programmable neuroimaging meta-analysis, annotation, decoding, and meta-analytic coactivation modeling in Python.
EEG Analysis
EEG analysis often includes filtering, artefact rejection, independent component analysis, epoching, event-related potential analysis, and spectral or time-frequency analysis.
- EEGLAB is a widely used MATLAB environment for EEG and related electrophysiological data, including ICA, time-frequency analysis, artefact rejection, and group-level workflows.
- SPM includes EEG analysis tools and supports statistical modeling across multiple imaging modalities.
- FieldTrip is also commonly used for advanced EEG, MEG, and iEEG analysis, including source reconstruction, time-frequency analysis, and non-parametric statistics.
fNIRS Analysis
Functional near-infrared spectroscopy analysis typically includes signal quality checks, motion correction, conversion from light intensity to haemoglobin concentration changes, statistical modeling, and group-level summaries.
- Homer3 is an open-source MATLAB application for fNIRS analysis, including subject- and group-level processing workflows.
- AtlasViewer supports fNIRS probe registration, sensitivity visualization, and image reconstruction workflows alongside Homer.
MEG Analysis
MEG analysis requires careful preprocessing, sensor-level analyses, forward models, source reconstruction, time-frequency analysis, and statistics. Mainstream MEG workflows often use MATLAB- or Python-based toolboxes depending on the lab’s analysis pipeline.
- SPM supports MEG analysis within the same statistical framework used for MRI and EEG.
- FieldTrip is a mainstream MATLAB toolbox for MEG, EEG, and iEEG, with strong support for source reconstruction and time-frequency analysis.
- MNE-Python is a widely used Python package for MEG and EEG preprocessing, visualization, source estimation, connectivity, machine learning, and statistics.
Behavioural and General Data Analysis
Behavioural and physiological workflows also use general-purpose research tools for experiment delivery, data cleaning, modeling, visualization, and reproducible reporting.